Postoperative complications and readmissions rates are higher in minority and low socioeconomic status (SES) patients. Low SES is associated with frailty, one of the best predictors of 30-day postoperative complications and early hospital readmission. Despite their influence on health outcomes, frailty and social risk factors are not considered in risk adjustment for reimbursement and quality measures. CMS developed financial incentive- based programs to improve quality of care. Yet this strategy disproportionately penalizes minority-serving, major teaching and safety net hospitals (SNH), further constraining resources for the care of vulnerable populations. Our long-term goal is to use frailty and social risk factors to identify at-risk patients to design more effective clinical care pathways. Frailty can be derived retrospectively using the American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP) dataset. Data networks are powerful research tools that can be used to answer important questions. However, extracting data from EHR is challenging. The Patient-Centered Outcomes Research Institute (PCORI) developed 13 Clinical Data Research Networks (CDRN) that have considerable overlapping membership with Clinical Translational Science Award (CTSA) institutions. While steady progress has been made, multiple barriers exist to efficiently access and use data. We will engage 3 CTSA hubs, each members of a different CDRN, to locally merge identified datasets developing data accessing and linking strategies at diverse institutions for dissemination across sites within CDRNs and to ultimately perform similar studies across CDRNs. We will use the SMART IRB reliance platform to harmonize the regulatory approval process as much as possible for each step of this project to identify barriers to use in data networks. We propose the following Aims: 1) Determine the predictive power of ethnicity, race, SES, and frailty for postoperative complications, mortality and readmissions to improve risk adjustment at 3 CTSA/CDRNs 2) Estimate postoperative functional status using natural language processing (NLP) and machine learning algorithms on inpatient physical therapy (PT), occupational therapy (OT) and nursing notes for ACS NSQIP patients to predict long-term functional status 3) Develop methods to predict long-term loss of independence after major surgery 4) Determine hospital resource utilization stratified by SES, frailty and minority status The significance of our study is the incorporation of social risk factors, frailty and functional status in risk adjustment forming the basis for future interventions by targeting patients at the highest risk for postoperative complications and reducing health care disparities. Our innovative approach harnesses data sources at diverse institutions with the goal of disseminating these methods across 3 CDRNs and the CTSA network.